[R] lme and lmer df's and F-statistics again

[Julia S.]

Hi Peter,

thanks a lot for your help. Very much appreciated.

Cheers,

Julia



Peter Dalgaard wrote:
> 
> Julia S. wrote:
>> Hi there,
>>
>> thanks for your help. I did read Bates statement several times, and I am
>> very glad and thankful that many statisticians spend much time on this.
>> The
>> problem is, as Dieter pointed it out, that many "end users" often have to
>> use statistics without being able to fully understand the math behind it.
>> Because if they would spend as much time on that as statisticians do,
>> they
>> wouldn't be able to do what they do where they use statistics for. 
>> And, no, I don't expect that a "simple" answer exists, but it might be
>> that
>> somebody had a similar problem like me before and may have a convincing
>> line
>> for a referee at hands. I have problems reformulating what I read here in
>> my
>> own words.  
>>
>> Dieter: when you write:
>> "but to use lme instead when possible" do you mean that when using lme
>> the
>> F-stats are correct? Because I assumed that the problem would be the same
>> with lme. 
>>
>> Julia
>>   
> They aren't... And they can be badly wrong in some cases.
> 
> At this stage, I think the best one can do is to get a feeling for
> whether the DF would be "large" and if so,  convince the referee to
> accept an asymptotic chi-square test (Wald or LRT type).
> 
> I think that the rationale for requiring authors to state the DF is not
> so much that journals believe in mighty SAS, but that they want to be
> able to catch completely wrong analyses, like when people compare two
> groups of each 5 rats and get a denominator DF of around 100 because
> there were 10 (correlated) measurements on each rat and no between-rats
> variation in the model.
> 
> As for figuring out whether or not you have large DF; if you have a
> nearly balanced design. it might be worth looking into what aov() says
> would be the DF for the same model with balanced data.
> 
> (And in any case, all DF-type corrections are in a sense wrong because
> they depend on 3rd and 4th moments of the Gaussian distribution, and
> your data probably aren't perfectly Gaussian.)
> 
> -- 
>    O__  ---- Peter Dalgaard             Øster Farimagsgade 5, Entr.B
>   c/ /'_ --- Dept. of Biostatistics     PO Box 2099, 1014 Cph. K
>  (*) \(*) -- University of Copenhagen   Denmark      Ph:  (+45) 35327918
> ~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk)              FAX: (+45) 35327907
> 
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